Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks

Abstract

Artificial intelligence (AI) is increasingly integrated into pediatric healthcare, offering opportunities to improve diagnostic accuracy and clinical decision-making. However, the complexity and opacity of many AI models raise concerns about trust, transparency, and safety, especially in vulnerable pediatric populations. Explainable AI (XAI) aims to make AI-driven decisions more interpretable and accountable. This review outlines the role of XAI in pediatric surgery, emphasizing challenges related to bias, the importance of ethical frameworks, and the need for standardized benchmarks. Addressing these aspects is essential to developing fair, safe, and effective AI applications for children. Finally, we provide recommendations for future research and implementation to guide the development of robust and ethically sound XAI solutions.

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